Air Pollution, Socioeconomic Status, and Age-Specific Mortality Risk in the United States

Key Points Question What are the associations among exposure to fine particulate matter with diameters 2.5 μm or smaller (PM2.5), age-specific mortality risk (ASMR), and socioeconomic status (SES) when disaggregating data for US census tracts, states and counties? Findings This cross-sectional study that included data from 67 148 census tracts found associations between PM2.5 and ASMR across all age groups and that the magnitude of this association was higher in the census tracts with lowest SES. Meaning These findings suggest that air pollution is an important factor associated with mortality, and public policies aiming to reduce mortality and inequalities must take small geographic units, like census tracts, into account.


Introduction
Exposure to ambient fine particulate matter (mass concentrations of particles Յ2.5 μm in diameter [PM 2.5 ]) is associated with increased mortality and morbidity and reduced life expectancy. [1][2][3][4] Key findings on the negative outcomes associated with long-term exposure to PM 2. 5 have come from cohort studies that have followed fixed sets of individuals over time. [5][6][7] In most studies, these individuals are not representative of the national US population. Studies relying on administrative data that are more representative of the US population as a whole have also found overwhelming evidence of negative outcomes associated with long-term exposure to PM 2.5 8-11 and the public health benefits associated with the overall decline of PM 2.5 concentrations over the years. [12][13][14] Although ecological regression analyses relying on administrative data have several limitations, eg, they are unable to adjust for individual-level risk factors and confusion between ecological associations and individual-level associations may present an ecological fallacy, they still may allow us to draw conclusions about the outcomes associated with pollution at the area level, which are important for policy makers. 15 It is also well known that exposure to particulate matter pollution varies substantially by geography and socioeconomic status (SES). 16,17 Economic activity, topography, climatic conditions, and industrial and population density in a region are important factors that modulate PM 2.5 emissions. 14 Environmental justice advocates have long pointed out that in the US, Black individuals experience disproportionately higher PM 2.5 concentrations than White individuals. 18  Mortality levels in different age groups also significantly vary over geography and SES in the US.
Few studies have investigated the magnitude of mortality and its association with air pollution by disaggregating data to smaller geographic spaces, such as census tracts, despite research showing that the finer the spatial resolution of the study, the more pronounced were disparities in PM 2.5 exposure by race and ethnicity. 19 One such study found that in the state of New Jersey, the association of PM 2.5 with mortality was significantly modified in census tracts with more Black residents, lower home values, and lower median incomes. 10 However, these studies failed to explicitly model the multilevel structure of the data. Recent studies have demonstrated that the total variance of life expectancy in the US is distributed very differently across geographical regions, and not incorporating such data hierarchy in an analysis limits the interpretability of study findings. 20,21 To our knowledge, there have been no previous studies that have analyzed the probability of death and its association with PM 2.5 via disaggregating data for census tracts and applying multilevel analysis.
This study aims to partition the variations in age-specific mortality risk (ASMR) and PM 2.5 at 3 geographic scales: states, counties, and census tracts. We also explore the interaction of SES in the association between PM 2.5 and ASMR using multilevel models. Our approach thus enables a more precise identification of populations with higher risk in terms of geographic scale and SES.

Age-Specific Mortality Risk
Data for the probability of death in each census tract between ages younger than 1 year, 1 to 4 years, Briefly, the population data used to estimate the probability of death were derived from the 2010 decennial census and from the American Community Survey (ACS) for the period 2011 to 2015.
The death records that occurred in this period were obtained from the National Vital Statistics System and were geocoded using the residential addresses of decedents to identify the census tracts. Life tables were calculated for each census tract with a minimum pooled population size of 5000 inhabitants in the period (2010-2015). Death counts were available for all age groups (as observed or estimated), with coherent age patterns and SEs of mortality. When zero death counts were observed in any age group in a census tract, the value was replaced with an estimated number based on a combination of demographic, socioeconomic, and geographic characteristics included in the models. 22 The definition of the probability of death between 2 exact ages is presented in concentrations within a given tract.

Socioeconomic and Demographic Covariates
Data on census tract-level SES and demographic factors are from the Opportunity Insights database. 24  ASMR was analyzed as a continuous variable with multilevel linear models, including random effects for states, counties, and census tracts. First, we used null models to estimate the crude variation in ASMR at each level. The proportion of variance attributed to each level was computed as the division of the observed variance at that level by the sum of the observed variances in the 3 analyzed levels. The quotient obtained was multiplied by 100. Then, we included census tract PM 2.5 and SES and demographic characteristics in models to estimate how much of the variation observed in each level may be explained by these variables. As sensitivity analyses, we also calculated mean PM 2.5 concentrations from 2000 to 2015, and in addition to exploring PM 2.5 concentrations in deciles, we ran linear multilevel models using this as a continuous variable. We stratified this analysis for each SES variable for people older than 45 years.
Finally, we mapped the geographical distribution of all analyzed variables. Data were analyzed using Stata version 15.1 (StataCorp), and maps were plotted using QGIS version 3.10.1.0 (QGIS Project). A 2-tailed P < .05 was considered significant. Data were analyzed in 2021.

Regional Distribution of ASMR, PM 2.5 , and SES Characteristics
Data from 67 148 census tracts nested in 3087 counties and 50 states were analyzed. ASMR varied substantially across census tracts. Large differences were observed in all age groups, from children to older adults. Analyzing the extreme age groups, among children younger than 1 year, the probability We also observed an important difference when we analyzed the proportion of residents below the federal poverty line and population density, with higher values observed in the highest air pollution deciles (eTable 4 in the Supplement). The adjusted model demonstrated that higher population density, higher proportions of Black residents and residents below the poverty line, lower household income, and lower education levels were associated with higher PM 2.5 concentrations. White areas indicate no available data. ASMR in census tracts also varied significantly based on the corresponding PM 2.5 deciles ( Table 3).
These probabilities were higher in the highest deciles of air pollution in all age groups. The greatest absolute difference was observed between decile 9 of PM 2.5 (second highest) and decile 1 of PM 2.5 (lowest) in the group aged 75 to 84 years, at 54.59 deaths per 1000 population. In relative terms, we highlight that in the group aged 45 to 54 years, the probability of death was 45% higher in decile 9 compared with decile 1. Interestingly, in all ages, the probability of death was greater in decile 9 and decile 8 than decile 10.
Associations between PM 2.5 deciles at the census tract level and ASMR, with and without adjusting for SES parameters (ie, proportion of Black residents, proportion of residents aged 25 years or older with a college degree, median household income, proportion of residents below the federal poverty line, and population density) in the multilevel model were observed for all age groups (      The findings in this study were robust when assessed using alternative periods of PM 2.5 (eTable 6 in the Supplement).
Analyses stratified by SES showed that the highest β values were observed in census tracts with the lowest median household income quintile, lowest quintile of college-educated residents older than 25 years, the highest quintile of the proportion of Black residents, highest quintile of residents below the federal poverty line, and highest population density (eTables 7-11 in the Supplement).

Discussion
This cross-sectional study presents a systematic and comprehensive analysis of the association among PM 2.5 concentration, SES, and ASMR across census tracts in the US. We report 6 key findings. concentrations. Fifth, the fully adjusted multilevel models showed a robust association between air pollution and ASMR across all age groups, particularly in the groups older than 45 years. Sixth, the risk of ASMR associated with PM 2.5 was higher in the most underprivileged census tracts.
The robust association between long-term exposure to PM 2.5 concentrations and the risk of mortality is consistent with epidemiological evidence. 7,25 Different pathways may explain the increased risk of mortality among people exposed to higher concentrations of PM 2.5 . PM 2.5 particles have the ability to penetrate the respiratory system and directly enter the bloodstream and specific organs, aggravating local oxidative stress and inflammation. Inflammation-related cytokine genes are stimulated, and inflammatory injuries may occur. Additionally, inflammatory cells and cytokines can damage lung cells synergistically. 26 According to a review by Du et al, 27 such a systemic inflammatory process is a risk factor associated with atherosclerosis progression, and the cascade of events associated with it may exacerbate myocardial ischemia. Other mechanisms through which PM 2.5 damages the body include cell injuries from free-radical peroxidation and imbalanced intracellular calcium homeostasis. 26 A 2008 study 28 reported that PM 2.5 particles may damage DNA and suppress DNA repair. Given the biological outcomes associated with exposure to air pollutions, it is expected that the clinical outcomes among older adults would be more acute that those among younger people, considering the potential for longer exposures to pollutants and many other health hazards, which may have synergic action, and less capacity for the body to biologically respond to the challenges imposed by pollution in older adults. Similarly, individuals with lower SES have a higher burden of diseases, more body cell and tissue damage, and more obstacles that limit access to health services, a healthy diet, and healthy habits and behaviors. mortality may be modified by location. The important roles of SES demonstrate that policies aimed at reducing pollutants in the US should consider not only the overall emission reductions but also racial, spatial, and socioeconomic inequalities.
The inequality in exposure to PM 2.5 associated with SES observed in our study is consistent with that observed in previous studies that also found that Black individuals and individuals with low incomes had the highest exposures. [30][31][32]  What we can observe from these findings is that public policies to improve air quality in the US need to be equitable. The social, labor, geographic, and economic contexts in which populations live need to be considered when designing actions to combat air pollution. As SES is associated with risk of exposure to environmental hazards, improving air quality involves improving the social determinants of health.

Limitations
This study has several limitations that need to be considered when analyzing the results. First, it was a cross-sectional study that used spatially aggregated data. Therefore, we cannot establish causal relationships between PM 2.5 exposure and ASMR. We were unable to adjust for individual-level behavioral and biological confounders in our models. Second, the geographic units used were politically and administratively defined and may not accurately represent the exposure of the inhabitants of a given region to the variables analyzed. Third, not all American census tracts were analyzed. Those who didn't meet methodological criteria were excluded. Fourth, the estimated PM 2.5 concentrations have some exposure measurement error; however, the estimation models have good estimation accuracy. Fifth, to estimate the risk of mortality in each age group in some census tracts, it was necessary to calculate the probability of death owing to missing values. Such calculations were based on the combination of socioeconomic and demographic characteristics of the census tracts; this may have affected the estimated associations. 20 We could not exclude census tracts with longterm care facilities for older adults that may have an inflated number of deaths, which may have inflated the variance at the census tract level. Hence, it is advisable to be cautious with population estimates made using small populations and territorial content data, such as census tracts.

Conclusions
The findings of this cross-sectional study suggest that efforts to increase in life expectancy in the US in the future should involve, in part, lower exposure of its population to air pollution. The greater risk of mortality in regions with higher levels of PM 2.5 across all age groups suggests that improving air quality is urgently needed in the US. Moreover, the observation that PM 2.5 is unevenly distributed in the US, with higher concentrations in the most underprivileged regions, suggests the need for more equitable policies on overall air improvement.

JAMA Network Open | Environmental Health
Air Pollution, Socioeconomic Status, and Age-Specific Mortality Risk in the United States